From Lab to Fab: Accelerating Industrial Innovation and Automation with Lab-on-a-Chip Technologies

This Research Topic (RT) aims to bridge the gap between cutting-edge academic research and real-world industrial applications in Lab-on-a-Chip (LOC) technologies. Bringing together leaders from academia, R&D, and industry, the RT will showcase how LOC is transforming sectors such as:- Pharmaceuticals & drug screening- Diagnostics & point-of-care testing- Food and beverage quality control- Environmental monitoring- Advanced manufacturing & process automationBy highlighting AI-driven automation, scalable production, and integration with IoT/Industry 4.0 systems, this topic will further enhance dialogue, collaborative innovation, and technology transfer between research labs and industry practitioners.Key Themes:- AI-Driven Automation in LOC Platforms: Advances in automated data acquisition, analysis, and real-time decision-making. See Nature's review: Trends in AI and automation in microfluidics- Scalable Manufacturing & Commercialization: Case studies and methods for transitioning LOC prototypes to industrial production and market adoption.- Integrated Sensing & IoT Connectivity: Embedding wireless, cloud-enabled monitoring and distributed analytics into chips for smart manufacturing.- Quality Assurance, Regulatory, and Standardization: Harmonization strategies and digital quality management leveraging AI/ML.- Success Stories: Industry-academia collaborations that have led to breakthrough LOC deployments.This Research Topic seeks to accelerate the translation of advanced Lab-on-a-Chip research into high-impact industrial and commercial applications. Submissions are invited that:- Present technology-driven automation solutions for sample processing, assay optimization, and high-throughput data analytics, reducing manual interventions and increasing reproducibility.- Demonstrate how digital twins, simulation, and computational modeling streamline LOC design, performance prediction, and manufacturing scale-up.- Describe the merging of LOC platforms with cloud connectivity, edge computing, and IoT - enabling real-time monitoring, predictive maintenance, and seamless data integrity across distributed production sites.- Showcase the use of robotic microfluidics and smart instrumentation to enable continuous, unattended operation - achieving 24/7 industrial analytics or manufacturing with minimal labor.- Integrate machine learning and big data analytics to reveal insights from complex biological, chemical, or clinical datasets generated by LOC systems.- Explore automated quality control using machine vision, AI-driven fault detection, and in-line statistical process control, driving regulatory compliance and reliability.- Highlight efforts in standardization, modular design, and plug-and-play interoperability - facilitating rapid system integration, upgrades, and scaling for diverse industrial environments.To empower both academic and industrial users, the RT will highlight:- AI Optimization Engines: Deep learning and reinforcement learning solutions for protocol optimization, process scheduling, and yield improvement.- Self-Adaptive LOC Platforms: Systems that automatically adapt workflows in response to real-time sensor feedback, minimizing errors and downtime.- Digital Engineering & Automation Pipelines: End-to-end software tools - from CAD for LOC layout, to automated experiment setup, remote monitoring, and digital documentation - enabling faster, “hands-off” R&D and deployment.- Open Access Repositories & Digital Twins: Promoting shared, AI- and simulation-ready datasets/models to accelerate cross-sector innovation and reproducibility.o Interfacing with Robotics: Use of programmable robots for reagent handling, sample sorting, or cartridge exchange, limiting manual labor and contamination risk.- Secure, Scalable Data Infrastructure: Mechanisms for collecting, warehousing, and mining LOC system data for both operational insights and regulatory traceability.Contributions demonstrating these technologies - especially with clear metrics for decreased manual effort, enhanced reliability, or new business/clinical value - will be particularly encouraged.Suggested Manuscript Types- Original industrial case studies- Reviews of industry standards and trends- Technical papers on automation, integration, and scaling- Perspective articles and policy discussions- Short communications showcasing rapid prototyping or deployment successes Topic editors: Xiangchun Xuan, Divesh Baxani Kamal, Stefan Köstler Source: https://www.frontiersin.org/research-topics/74711/from-lab-to-fab-accelerating-industrial-innovation-and-automation-with-lab-on-a-chip-technologies